• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.11
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• pp.132-139
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• 1993

This paper presents a new performance-driven multi-levelizer which transforms a two-level description into a boolean network of the multilevel structure satisfied with user's costraints, such as chip area, the number of wires and literals, maximum delay, function level, fanin, fanout, etc.. The performance of circuits is estimated by reference to the informations in cell library through the cell mapping phase, and multi-levelization of circuits is constructed by the decomposition using the kernel and factoring concepts. Here, the saving cost of a common subexpression is defined to the sum of area and delay saved, when it is substituted. The experiments with MCNC benchmarks show the efficiency of the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.832-834
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• 2005

임베디드 시스템은 범용 컴퓨팅 시스템과 달리 자신을 포함하고 있는 기기에 부과된 특정 목적의 컴퓨팅 작업만을 수행한다. 이 시스템을 제어하기 위해서 운영체제가 필요로 하며, 임베디드 환경에서는 신뢰성과 정확성을 요하는 부분이 많기 때문에 실시간 운영체제를 필요로 한다. Real-Time kernel을 기반으로 하는 MicroC/OS-II는 수많은 용도로 사용되고 있지만 task 사용에 한계가 있다. 이 논문에서 제안하는 스케줄링은 task의 생성 수를 늘려주지만, 이 경우 task간의 우선순위 설정이 어려워진다. 이 문제 해결을 위해서 task들의 우선순위 결정은 deadline을 이용하여 3레벨로 나눈다. 3레벨로 나누어지면 task의 수가 증가해도 개발자는 task들을 레벨에 맞게 설정하면 task 관리로 인하여 생기는 문제를 줄일 수 있으며, 효율적인 스케줄링을 가능하게 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.387-390
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• 2024

본 논문에서는 효율적인 2차 오차 함수를 이용하여 입자 기반에서 EMC(Extended Marching Cubes) 알고리즘을 구현할 수 있는 새로운 알고리즘을 제안한다. Smoothing 커널(Kernels)을 통해 계산한 입자 평균 위치에서 레벨셋(Level-set)을 계산해 스칼라장을 구축한다. 그리고 난 뒤 SPH(Smoothed particle hydrodynamics)기반의 커널을 통해 밀도, 입자 평균 위치를 계산한다. 스칼라장을 이용해 등가 곡면(Isosurface)을 찾고 음함수로 표현된 표면을 구성한다. SPH 커널을 공간에서 미분하면 공간상의 어느 위치에서나 기울기를 계산할 수 있고, 이를 통해 얻어진 법선벡터를 이용하여 일반적인 EMC나 DC(Dual contouring)에서 사용하는 2차 오차 함수를 효율적으로 설계한다. 결과적으로 제안하는 방법은 메쉬와 같이 연결정보다 없는 입자 기반 데이터에서도 EMC 알고리즘을 구현하여 볼륨(Volume) 손실을 줄이고, 복잡한 음함수 표면을 표현할 수 있게 한다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.1
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• pp.101-108
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• 2014

This study was conducted to investigate the effect of nitrogen rates on the growth characteristics and yield of maize in agricultural fields with the stream. This indicates the necessity and optimal level of nitrous fertilization to examine the possibilities of quantity enhancement. Plant height and ear height of maize were not significantly different among the nitrogen rates. Stem diameter and leaf area index increased in the nitrogen treatment compared to untreated control. Changes of photosynthetic rate in maize leaves depending on nitrogen treatments increased as much as nitrogen rates were increased up to the highest level, 36 kg per 10a. NDF and ADF content levels of maize were investigated with different nitrogen rates regardless of treatments. In the case of NDF, it showed a tendency to decrease after 8 days of tasseling date. ADF had also decreased after 15 days of tasseling date. Nitrogen uptake of maize leaves with different nitrogen rates showed the highest level, $4.9g\;kg^{-1}$ with 36 kg per 10a on the tasseling date. Ear length and 100-kernel weight, there were no significant differences according to yield and the components with different nitrogen rates. Ear diameter and kernel number, nitrogen rates of 18 kg and 36 kg were increased compared to nitrogen rate of 9 kg per 10a and untreated control. The pericarps in 9 kg nitrogen rate and control were thicker than those of 18 kg and 36 kg treatment. The yield, 18 kg, 36 kg, and 9 kg treatments were increased by 10.96%, 9.27%, and 3.31%, compared to control. The component analysis on maize kernel with different nitrogen rates, starch showed no significant differences among treatments. Total sugar in 18 kg nitrogen treatment represented the highest content level, 6.37%. In addition, Amylopectin in 18 kg treatment showed the highest content level of 90.38%. However, amylose in 18 kg treatment showed the lowest level, 9.62% which drew a conclusion that waxy of 18 kg treatment is considered to be the strongest one. From the results described above, nitrous fertilization is essential to grow maize in agricultural fields with the stream. The optimum level of nitrous fertilization is considered 18 kg per 10a.

• Nuclear Engineering and Technology
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• v.39 no.2
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• pp.133-142
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• 2007

The Electric Power Research Institute (EPRI) demonstrated a method for monitoring the performance of instrument channels in Topical Report (TR) 104965, 'On-Line Monitoring of Instrument Channel Performance.' This paper presents the results of several models originally developed by EPRI to monitor three nuclear plant sensor sets: Pressurizer Level, Reactor Protection System (RPS) Loop A, and Reactor Coolant System (RCS) Loop A Steam Generator (SG) Level. The sensor sets investigated include one redundant sensor model and two non-redundant sensor models. Each model employs an Auto-Associative Kernel Regression (AAKR) model architecture to predict correct sensor behavior. Performance of each of the developed models is evaluated using four metrics: accuracy, auto-sensitivity, cross-sensitivity, and newly developed Error Uncertainty Limit Monitoring (EULM) detectability. The uncertainty estimate for each model is also calculated through two methods: analytic formulas and Monte Carlo estimation. The uncertainty estimates are verified by calculating confidence interval coverages to assure that 95% of the measured data fall within the confidence intervals. The model performance evaluation identified the Pressurizer Level model as acceptable for on-line monitoring (OLM) implementation. The other two models, RPS Loop A and RCS Loop A SG Level, highlight two common problems that occur in model development and evaluation, namely faulty data and poor signal selection

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.326-334
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• 2016

In this paper for developing and implementing the realtime HD level video streaming transmitter which is operated on the multi-platform in all network and client environment compared to the exist video live streaming transmitter. We design the realtime HD level video streaming transmitter supporting the multi-platform using the TMS320DM386 video processor of T.I company and then porting the Linux kernel 2.6.29 and implementing the RTSP(Real Time Streaming Protocol)/RTP(Real Time Transport Protocol), HLS(Http Live Streaming), RTMP(Real Time Messaging Protocol) that can support the multi-platform of video stream protocol of the received equipments (smart phone, tablet PC, notebook etc.). For proving the performance of developed video streaming transmitter, we make the testing environment for testing the performance of streaming transmitter using the notebook, iPad, android Phone, and then analysis the received video in the client displayer. In this paper, we suggest the developed the Realtime HD(High Definition) level Video Streaming transmitter performance data values higher than the exist products.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.451-460
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• 2005

We present a methodology to design energy-efficient data paths using FPGAs. Our methodology integrates domain specific modeling, coarse-grained performance evaluation, design space exploration, and low-level simulation to understand the tradeoffs between energy, latency, and area. The domain specific modeling technique defines a high-level model by identifying various components and parameters specific to a domain that affect the system-wide energy dissipation. A domain is a family of architectures and corresponding algorithms for a given application kernel. The high-level model also consists of functions for estimating energy, latency, and area that facilitate tradeoff analysis. Design space exploration(DSE) analyzes the design space defined by the domain and selects a set of designs. Low-level simulations are used for accurate performance estimation for the designs selected by the DSE and also for final design selection We illustrate our methodology using a family of architectures and algorithms for matrix multiplication. The designs identified by our methodology demonstrate tradeoffs among energy, latency, and area. We compare our designs with a vendor specified matrix multiplication kernel to demonstrate the effectiveness of our methodology. To illustrate the effectiveness of our methodology, we used average power density(E/AT), energy/(area x latency), as themetric for comparison. For various problem sizes, designs obtained using our methodology are on average $25\%$ superior with respect to the E/AT performance metric, compared with the state-of-the-art designs by Xilinx. We also discuss the implementation of our methodology using the MILAN framework.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.1
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• pp.1-8
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• 2014

Using the level set and the meshfree methods, we develop a topological shape optimization method applied to linear elasticity problems. Design gradients are computed using an efficient adjoint design sensitivity analysis(DSA) method. The boundaries are represented by an implicit moving boundary(IMB) embedded in the level set function obtainable from the "Hamilton-Jacobi type" equation with the "Up-wind scheme". Then, using the implicit function, explicit boundaries are generated to obtain the response and sensitivity of the structures. Global nodal shape function derived on a basis of the reproducing kernel(RK) method is employed to discretize the displacement field in the governing continuum equation. Thus, the material points can be located everywhere in the continuum domain, which enables to generate the explicit boundaries and leads to a precise design result. The developed method defines a Lagrangian functional for the constrained optimization. It minimizes the compliance, satisfying the constraint of allowable volume through the variations of boundary. During the optimization, the velocity to integrate the Hamilton-Jacobi equation is obtained from the optimality condition for the Lagrangian functional. Compared with the conventional shape optimization method, the developed one can easily represent the topological shape variations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.821-834
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• 2019

As cyber security threats increase, there is a growing demand for highly reliable systems. Common Criteria, an international standard for evaluating information security products, requires formal specification and verification of the system to ensure a high level of security, and more and more cases are being observed. In this paper, we propose highly reliable drone systems that ensure high level security level and trust. Based on the results, we use formal methods especially Z/EVES to improve the system model in terms of scheduling in the system kernel.